by    in Data Science, prediction

Recent Celebrity Deaths as Predicted by the Wisdom of Ranker Crowds

At the end of each year, there are usually media stories that compile lists of famous people who have passed away. These lists usually cause us to pause and reflect. Lists like Celebrity Death Pool 2013 on Ranker, however, give us an opportunity to make (macabre) predictions about recent celebrity deaths.

We were interested in whether “wisdom of the crowd” methods could be applied to aggregate the individual predictions. The wisdom of the crowd is about making more complete and more accurate predictions, and both completeness and accuracy seem relevant here. Being complete means building an aggregate list that identifies as many celebrity deaths as possible. Being accurate means, in a list where only some predictions are borne out, placing those who do die near the top of the list.

Our Ranker data involved the lists provided by a total of 27 users up until early in 2013. (Some them were done after at least one celebrity, Patti Page, had passed away, but we thought they still provided useful predictions about other celebrities). Some users predicted as many as 25 deaths, while some made a single prediction. The median number of predictions was eight, and, in total, 99 celebrities were included in at least one list. At the time of posting, six of the 99 celebrities have passed away.

One way to measure how well a user made predictions is to work down their list, keeping track of every time they correctly predicted a recent celebrity death. This approach to scoring is shown for all 27 users in the graph below. Each blue circle corresponds to a user, and represents their final tally. The location of the circle on the x-axis corresponds to the total length of their list, and the location on the y-axis corresponds to the total number of correct predictions they made. The blue lines leading up to the circles track the progress for each user, working down their ranked lists. We can see that the best any user did was predict two out or the current six deaths, and most users currently have none or one correct predictions in their list.

To try and find some wisdom in this crowd of users, we applied an approach to combining rank data developed as part of our general research into human decision-making, memory, and individual differences. The approach is based on classic models in psychology that go all the way back to the work of Thurstone in 1931, but has some modern tweaks. Our approach allows for individual differences, and naturally identifies expert users, upweighting their opinions in determining the aggregated crowd list. A paper describing the nuts and bolts of our modeling approach can be found here (but note we used a modified version for this problem, because users only provide their “Top-N” responses, and they get to choose N, which is the length of their list).

The net result of our modeling is a list of all 99 celebrities, in an order that combines the rankings provided by everybody. The top 5 in our aggregated list, for the morbidly curious, are Hugo Chavez (already a correct prediction), Fidel Castro, Zsa Zsa Gabor, Abe Vigoda, and Kirk Douglas. We can assess the wisdom of the crowd in the same way we did individuals, by working down the list, and keeping track of correct predictions. This assessment is shown by the green line in the graph below. Because the list includes all 99 celebrities, it will always find the six who have already recently passed away, and the names of those celebrities are shown at the top, in the place they occur in the aggregated list.

Recent Celebrity Deaths and Predictions

The interesting part assessing the wisdom of the crowd is how early in the list it makes correct predictions about recent celebrity deaths. Thus, the more quickly the green line goes up as it moves to the right, the better the predictions of the crowd. From the graph, we can see that the crowd is currently performing quite well, and is certainly about the “chance” line, represented by the dotted diagonal. (This line corresponds to the average performance of a randomly-ordered list).

We can also see that the crowd is performing as well as, or better than, all but one of the individual users. Their blue circles are shown again along with crowd performance. Circles that lie above and to the left of the green line indicate users outperforming the crowd, and there is only one of these. Interestingly, predicting celebrity deaths by using age, and starting with the oldest celebrity first, does not perform well. This seemingly sensible heuristic is assessed by the red line, but is outperformed by the crowd and many users.

Of course, it is only May, so the predictions made by users on Ranker have time to be borne out. Our wisdom of the crowd predictions are locked in, and we will continue to update the assessment graphs.

– Michael Lee

An Opinion Graph of the World’s Beers

One of the strengths of Ranker‘s data is that we collect such a wide variety of opinions from users that we can put opinions about a wide variety of subjects into a graph format.  Graphs are useful as they let you go beyond the individual relationships between items and see overall patterns.  In anticipation of Cinco de Mayo, I produced the below opinion graph of beers, based on votes on lists such as our Best World Beers list.  Connections in this graph represent significant correlations between sentiment towards connected beers, which vary in terms of strength.  A layout algorithm (force atlas in Gephi) placed beers that were more related closer to each other and beers that had fewer/weaker connections further apart.  I also ran a classification algorithm that clustered beers according to preference and colored the graph according to these clusters.  Click on the below graph to expand it.

Ranker's Beer Opinion Graph

One of the fun things about graphs is that different people will see different patterns.  Among the things I learned from this exercise are:

  • •The opposite of light beer, from a taste perspective, isn’t dark beer.  Rather, light beers like Miller Lite are most opposite craft beers like Stone IPA and Chimay.
  • •Coors light is the light beer that is closest to the mainstream cluster.  Stella Artois, Corona, and Heineken are also reasonable bridge beers between the main cluster and the light beer world.
  • •The classification algorithm revealed six main taste/opinion clusters, which I would label: Really Light Beers (e.g. Natural Light), Lighter Mainstream Beers (e.g. Blue Moon), Stout Beers (e.g. Guinness), Craft Beers (e.g. Stone IPA), Darker European Beers (e.g. Chimay), and Lighter European Beers (e.g. Leffe Blonde).  The interesting parts about the classifications are the cases on the edge, such as how Newcastle Brown Ale appeals to both Guinness and Heineken drinkers.
  • •Seeing beers graphed according to opinions made me wonder if companies consciously position their beers accordingly.  Is Pyramid Hefeweizen successfully appealing to the Sam Adams drinker who wants a bit of European flavor?  Is Anchor Steam supposed to appeal to both the Guinness drinker and the craft beer drinker?  I’m not sure if I know enough about the marketing of beers to know the answer to this, but I’d be curious if beer companies place their beers in the same space that this opinion graph does.

These are just a few observations based on my own limited beer drinking experience.  I tend to be more of a whiskey drinker, and hope more of you will vote on our Best Tasting Whiskey list, so I can graph that next.  I’d love to hear comments about other observations that you might make from this graph.

– Ravi Iyer

Ranker Uses Big Data to Rank the World’s 25 Best Film Schools

NYU, USC, UCLA, Yale, Julliard, Columbia, and Harvard top the Rankings.

Does USC or NYU have a better film school?  “Big data” can provide an answer to this question by linking data about movies and the actors, directors, and producers who have worked on specific movies, to data about universities and the graduates of those universities.  As such, one can use semantic data from sources like Freebase, DBPedia, and IMDB to figure out which schools have produced the most working graduates.  However, what if you cared about the quality of the movies they worked on rather than just the quantity?  Educating a student who went on to work on The Godfather must certainly be worth more than producing a student who received a credit on Gigli.

Leveraging opinion data from Ranker’s Best Movies of All-Time list in addition to widely available semantic data, Ranker recently produced a ranked list of the world’s 25 best film schools, based on credits on movies within the top 500 movies of all-time.  USC produces the most film credits by graduates overall, but when film quality is taken into account, NYU (208 credits) actually produces more credits among the top 500 movies of all-time, compared to USC (186 credits).  UCLA, Yale, Julliard, Columbia, and Harvard take places 3 through 7 on the Ranker’s list.  Several professional schools that focus on the arts also place in the top 25 (e.g. London’s Royal Academy of Dramatic Art) as well as some well-located high schools (New York’s Fiorello H. Laguardia High School & Beverly Hills High School).

The World’s Top 25 Film Schools

  1. New York University (208 credits)
  2. University of Southern California (186 credits)
  3. University of California – Los Angeles (165 credits)
  4. Yale University (110 credits)
  5. Julliard School (106 credits)
  6. Columbia University (100 credits)
  7. Harvard University (90 credits)
  8. Royal Academy of Dramatic Art (86 credits)
  9. Fiorello H. Laguardia High School of Music & Art (64 credits)
  10. American Academy of Dramatic Arts (51 credits)
  11. London Academy of Music and Dramatic Art (51 credits)
  12. Stanford University (50 credits)
  13. HB Studio (49 credits)
  14. Northwestern University (47 credits)
  15. The Actors Studio (44 credits)
  16. Brown University (43 credits)
  17. University of Texas – Austin (40 credits)
  18. Central School of Speech and Drama (39 credits)
  19. Cornell University (39 credits)
  20. Guildhall School of Music and Drama (38 credits)
  21. University of California – Berkeley (38 credits)
  22. California Institute of the Arts (38 credits)
  23. University of Michigan (37 credits)
  24. Beverly Hills High School (36 credits)
  25. Boston University (35 credits)

“Clearly, there is a huge effect of geography, as prominent New York and Los Angeles based high schools appear to produce more graduates who work on quality films compared to many colleges and universities,“ says Ravi Iyer, Ranker’s Principal Data Scientist, a graduate of the University of Southern California.

Ranker is able to combine factual semantic data with an opinion layer because Ranker is powered by a Virtuoso triple store with over 700 million triples of information that are processed into an entertaining list format for users on Ranker’s consumer facing website, Ranker.com.  Each month, over 7 million unique users interact with this data – ranking, listing and voting on various objects – effectively adding a layer of opinion data on top of the factual data from Ranker’s triple store. The result is a continually growing opinion graph that connects factual and opinion data.  As of January 2013, Ranker’s opinion graph included over 30,000 nodes with over 5 million edges connecting these nodes.

– Ravi Iyer

Predicting Box Office Success a Year in Advance from Ranker Data

A number of data scientists have attempted to predict movie box office success from various datasets.  For example, researchers at HP labs were able to use tweets around the release date plus the number of theaters that a movie was released in to predict 97.3% of movie box office revenue in the first weekend.  The Hollywood Stock Exchange, which lets participants bet on the box office revenues and infers a prediction, predicts 96.5% of box office revenue in the opening weekend.  Wikipedia activity predicts 77% of box office revenue according to a collaboration of European researchers.  Ranker runs lists of anticipated movies each year, often for more than a year in advance, and so the question I wanted to analyze in our data was how predictive is Ranker data of box office success.

However, since the above researchers have already shown that online activity at the time of the opening weekend predicts box office success during that weekend, I wanted to build upon that work and see if Ranker data could predict box office receipts well in advance of opening weekend.  Below is a simple scatterplot of results, showing that Ranker data from the previous year predicts 82% of variance in movie box office revenue for movies released in the next year.

Predicting Box Office Success from Ranker Data
Predicting Box Office Success from Ranker Data

The above graph uses votes cast in 2011 to predict revenues from our Most Anticipated 2012 Films list.  While our data is not as predictive as twitter data collected leading up to opening weekend, the remarkable thing about this result is that most votes (8,200 votes from 1,146 voters) were cast 7-13 months before the actual release date.  I look forward to doing the same analysis on our Most Anticipated 2013 Films list at the end of this year.

– Ravi Iyer

by    in Data Science

Crowdsourcing Objective Answers to Subjective Questions – Nerd Nite Los Angeles

A lot of the questions on Ranker are subjective, but that doesn’t mean that we cannot use data to bring some objectivity to this analysis.  In the same way that Yelp crowdsources answers to subjective questions about restaurants and TripAdvisor crowdsources answers to subjective questions about hotels, Ranker crowdsources answers to a broader assortment of relatively subjective questions such as the Tastiest Pizza Toppings, the Best Cruise Destination, and the Worst Way to Die.

A few weeks ago, I did an informal talk on the Wisdom of Crowds approach that Ranker takes to crowdsource such answers at a Los Angeles bar as part of “Nerd Nite”.  The gist of it is that one can crowdsource objective answers to subjective questions by asking diverse groups of people questions in diverse ways.  Greater diversity, when aggregated effectively, enables the error inherent in answering any subjective question to be minimized.  For example, we know intuitively that relying on only the young or only the elderly or only people in cities or only people who live in rural areas gives us biased answers to subjective questions.  But when all of these diverse groups agree on a subjective question, there is reason to believe that there is an objective truth that they are responding to.  Below is the video of that talk.

If you want to see a more formal version of this talk, I’ll be speaking at greater length on Ranker’s methodologies at the Big Data Innovation Summit in San Francisco this Friday.

– Ravi Iyer

by    in interest graph, Opinion Graph

A Battle of Taste Graphs: Baltimore Ravens Fans vs. San Francisco 49ers Fans

Super Bowl Sunday is a day when two cities and two fan groups are competing for bragging rights, even as the Baltimore Ravens and San Francisco 49ers themselves do the playing.  You might be interested in understanding these teams’ fans better through an exploration of their fans’ taste graphs, from a recent post on our data blog, which examines correlations between votes on lists like the Top NFL Teams of 2012 and non-sports lists like our list of delicious vegetables (yum!).

For one, There is also absolutely zero consensus where music is concerned. 49er’s fans listen to an eclectic mixture of genres: up-and-coming rappers like Kendrick Lamar sit right next to INXS and 90s brit-poppers Pulp. Yet where the Ravens are concerned, classic rock is still king: Hendrix, CCR, and Neil Young are an undisputed top three. The 49ers also have the Ravens utterly beat in terms of culinary taste. Monterrey Jack and Cosmos are a fairly clear favorite among fans, while Baltimore’s stick to staples: Coffee, Bell peppers, and Ham are the only food items that correlated enough to even be tracked.

 A Snapshot from Ranker’s Data Mining Tool

TV tastes also varied between the two teams: Ravens fans stuck to almost exclusively comedic faire (Pinky and The Brain, Rugrats, Mythbusters and Louie correlated strongly), while the 49er’s stuck to more structured, dramatic shows, such as The Walking Deadand Dexter.

Read the full post here over on our data blog.

– Ravi Iyer

by    in Data Science, Pop Culture

On Touchdowns and Tastes: This Sunday’s Conflict Of Fan-Interests

 

helmet images courtesy of http://nfl-franchises.findthedata.org

 

The greatest moment of fear in my childhood came on the eve of my first ever family trip to Manhattan. It wasn’t the flight or the crowds or the crime rate that had seven-year-old me scared. I was terrified because I had been brought up to believe that any and all Yankees fans were villainous scum, lowest of the low, the nadir of human development. Visiting the city and actually interacting with people from New York had an effect on me akin to realizing that there wasn’t a Santa Claus: I was faced with the reality that not all Yankees fans are evil. It just wasn’t mathematically feasible. You can’t run a city of 8 million people without having some people who don’t suck. This, of course, is a key part of the unspoken acknowledgement all (nonviolent & sane) sports fans have; that sports fandom is a mostly regional thing, and that there’s no point in thinking those who back another team are truly inferior, or even all that different from you.

However, if you told that to anyone from Baltimore or San Francisco right now, they’d likely try to argue for the ideological superiority of their respective squad. With the Super Bowl literally on the horizon, this is not a time where people deal in shades of gray. But are there any real, quantifiable differences between the fans of the Ravens and the 49ers? Anything else on the line in this contest?

Weirdly enough, yes. The Ranker correlation data for supporters of the Ravens and the 49ers is strikingly dissimilar. You’d think that there would be some commonalities between the likes and dislikes of the two teams, even just those that stem from the demographic features of “football fans”. But no, the pop culture tastes of the two teams have a strikingly miniscule amount of overlap.  Let us examine some of the correlations based on user behavior at Ranker.com.

For one, There is also absolutely zero consensus where music is concerned. 49er’s fans listen to an eclectic mixture of genres: up-and-coming rappers like Kendrick Lamar sit right next to INXS and 90s brit-poppers Pulp. Yet where the Ravens are concerned, classic rock is still king: Hendrix, CCR, and Neil Young are an undisputed top three. The 49ers also have the Ravens utterly beat in terms of culinary taste. Monterrey Jack and Cosmos are a fairly clear favorite among fans, while Baltimore’s stick to staples: Coffee, Bell peppers, and Ham are the only food items that correlated enough to even be tracked.

 A Snapshot from Ranker’s Data Mining Tool

TV tastes also varied between the two teams: Ravens fans stuck to almost exclusively comedic faire (Pinky and The Brain, Rugrats, Mythbusters and Louie correlated strongly), while the 49er’s stuck to more structured, dramatic shows, such as The Walking Dead and Dexter.

Some of these differences can be explained away geographically (In-and-Out Burger, a prominent correlated item for the 49ers, isn’t going to appeal to anyone on the east coast since they just don’t have it), but when the data is stacked up, there is a very noticeable dissimilarity in interests between the two teams. One could, of course, use this data to try to advocate for the superiority of one team over the other (I won’t even get into the far more extensive video game tastes of the 49er’s). However, the far more intriguing question at hand lies in what we all really watch the Super Bowl for: the ads.

If, as the data suggests, there is such a difference between the interests of the average 49er’s fan and the average Ravens fan, how will the ads attempt to bridge this gap? Since I could give a damn about the score (neither team is the Pats, who cares), I’ll be keeping track instead of whose team’s interests are catered to by the adverts. On Sunday, one team will win on the field, and another during the commercials.

– Eamon Levesque

by    in Data Science, interest graph, Opinion Graph

The Opinion Graph predicts more than the Interest Graph

At Ranker, we keep track of talk about the “interest graph” as we have our own parallel graph of relationships between objects in our system, that we call an “opinion graph”.  I was recently sent this video concerning the power of the interest graph to drive personalization.

The points made in the video are very good, about how the interest graph is more predictive than the social graph, as far as personalization goes.  I love my friends, but the kinds of things they read and the kinds of things I read are very different and while there is often overlap, there is also a lot of diversity.  For example, trying to personalize my movie recommendations based on my wife’s tastes would not be a satisfying experience.  Collaborative filtering using people who have common interests with me is a step in the right direction and the interest graph is certainly an important part of that.

However, you can predict more about a person with an opinion graph versus an interest graph. The difference is that while many companies can infer from web behavior what people are interested in, perhaps by looking at the kinds of articles and websites they consume, a graph of opinions actually knows what people think about the things they are reading about.  Anyone who works with data knows that the more specific a data point is, the more you can predict, as the amount of “error” in your measurement is reduced.  Reduced measurement error is far more important for prediction than sample size, which is a point that gets lost in the drive toward bigger and bigger data sets.  Nate Silver often makes this point in talks and in his book.

For example, if you know someone reads articles about Slumdog Millionare, then you can serve them content about Slumdog Millionare.  That would be a typical use case for interest graph data. Using collaborative filtering, you can find out what other Slumdog Millionare fans like and serve them appropriate content.  With opinion graph data, of the type we collect at Ranker, you might be able to differentiate between a person who thinks that Slumdog Millionare is simply a great movie versus someone who thinks the soundtrack was one of the best ever.  If you liked the movie, we would predict that you would also like Fight Club.  But if you liked the soundtrack, you might instead be interested in other music by A.R. Rahman.

Simply put, the opinion graph can predict more about people than the interest graph can.

– Ravi Iyer

by    in Data Science

Mitt Romney Should Have Advertised on the X-Files

With the election recently behind us, many political analysts are conducting analyses of the campaigns, examining what worked and what didn’t.  One specific area where the Obama team is getting praise is in their unprecedented use of data to drive campaign decisions, and even more specifically, how they used data to micro-target fans who watched specific TV shows.  From this New York Times article concerning the Obama Team’s TV analytics:

“Culling never-before-used data about viewing habits, and combining it with more personal information about the voters the campaign was trying to reach and persuade than was ever before available, the system allowed Mr. Obama’s team to direct advertising with a previously unheard-of level of efficiency, strategists from both sides agree….

[They] created a new set of ratings based on the political leanings of categories of people the Obama campaign was interested in reaching, allowing the campaign to buy its advertising on political terms as opposed to traditional television industry terms…..

[They focused] on niche networks and programs that did not necessarily deliver large audiences but, as Mr. Grisolano put it, did provide the right ones.”

 

The Obama team focused more on undecided/apolitical voters in an effort to get them to the polls.  Given that some Mitt Romney supporters have blamed a lack of turnout of supporters for the results of the election, perhaps Romney would have been smart to have created a ranked list of TV shows, based on how much fans of the shows supported Romney, and then placed positive/motivating ads on those shows in an effort to increase turnout of his base.  Where would Romney get such data?  From Ranker!

Mitt Romney is on many votable Ranker lists (e.g. Most Influential People of 2012) and based on people who voted on those lists and also lists such as our Best Recent TV Shows list, we can examine which TV shows are positively or negatively associated with Mitt Romney.  Below are the top positive results from one of our internal tools.

As you can see, the X-Files appears to be the highest correlated show, by a fair margin.  I don’t watch the X-Files, so I wasn’t sure why this correlation exists, but I did a bit of research, and found this article exploring how the X-Files supported a number of conservative themes, such as the persistence of evil, objective truth, and distrust of government (also see here).  The article points out that in one episode, right wing militiamen are depicted as being heroic, which never would happen in a more liberal leaning plot.  Perhaps if you are a conservative politician seeking to motivate your base, you should consider running ads on reruns of the X-Files, or if you run a television station that shows X-Files reruns, consider contacting your local conservative politicians leveraging this data.

You may notice that this list contains more classic/rerun shows (e.g. Leave it to Beaver) than current shows.  This appears to be part of a general trend where conservatives on Ranker tend to positively vote for classic TV, a subject we’ll cover in a future blog post.  The possibility of advertising on reruns is part of what we would like to highlight in this post, as ads are likely relatively cheap and audiences can be more easily targeted, a tactic which the Obama campaign has been praised for.  At Ranker, we’re hopeful that more advertisers will seek value in the long-tail and mid-tail and will seek to mimic the tactics of the Obama campaign, as our data is uniquely suited for such psychographic targeting.

– Ravi Iyer

by    in Data Science

How Crowdsourcing can uncover Niche/Trending shows

At Ranker, people give us their opinions in various different ways. Some people vote.  Other people make long lists.  Still others make really short lists.  Some people tell us their absolute favorite things, while others list everything they’ve ever experienced.  One of the advantages of this diversity is that it allows us to examine patterns within these divergent types of opinions.  For example, some things are really popular, meaning that everyone lists them (e.g. Michael Jordan is on everyone’s best basketball players list).  Most popular things are also things that people generally list high on their lists and also get lots of positive votes (e.g. Michael Jordan).  However, there are some things that don’t get listed very often, but when they do get listed, people are passionate about them, meaning that they get listed high on people’s lists.  We highlight these items in our system using the niche symbol.

I’ve recently been examining our “niche” tag, which signifies when something is not particularly popular, but people are passionate about it.  There are many reasons why things can be niche.  Some things appeal specifically to younger (e.g. Rugrats) or older crowds (e.g.  The Rockford Files).  Other things have natural audiences (e.g.baseball fans who appreciate defense and think Ozzie Smith is one of the greatest players of all time).  The most interesting case is when something that I can’t identify starts showing on the niche list (see the list at the time of this writing here).

This is especially helpful for someone like me, who doesn’t always know what is ‘hot’ and naturally looks to data to find new quality entertainment.  Awhile back, the show Community consistently was showing highest on our niche algorithm.  Few people listed it as one of the best recent TV shows, but those who listed it tended to think very highly of it.  I was intruiged enough to watch the pilot on Hulu and have since become hooked.  Community has since graduated from our niche algorithm as it became popular.  Sometimes passion amongst a small group is how a trend starts.

As Margaret Mead believed that only a small group of citizens could change the world, so Malcolm Gladwell has shown how a small group of trendsetters can signal changes in pop culture.  Not everything on our niche list will become the next big thing, but it’s certainly a good place to search for candidates.

Among the things that people seem to be passionate about now, that aren’t so popular, are several good candidates for up and coming movies, bands, or TV shows.  Pappillon is currently hot, scoring over 2 standard deviations higher in terms of list position on our best movie list, despite being less popular than most movies.  Another Earth and 13 Assassins,  seem like potentially interesting and under the radar films from 2011. Real Time with Bill Maher‘s niche status may be due to appeal particular ideological group, but Warehouse 13 appealed to just my niche as it had passionate fans on both the best recent TV shows list and the best Sci-Fi TV shows list (it has since graduated from the list due to increased popularity).  Warehouse 13’s highest correlated show is one of my favorites, Battlestar Galactica, so I’m definitely going to check it out.

I tend to be a late adopter of pop culture, but thanks to the niche tag, maybe I can be a little hipper going forward.  Take a look at our niche items as of October 20, 2012 and any comments on other things to consider checking out would be appreciated. Or perhaps take a look in a few months time and consider whether our niche tag successfully captured coming trends in a few cases.

– Ravi Iyer